Visual media are increasingly generated, manipulated, and transmitted
by computers. When well designed, such displays capitalize on human
facilities for processing visual information and thereby improve
comprehension, memory, inference, and decision making. Yet the digital
tools for transforming data into visualizations still require
low-level interaction by skilled human designers. As a result,
producing effective visualizations can take hours or days and consume
considerable human effort.

In this course we will study techniques and algorithms for creating
effective visualizations based on principles and techniques from
graphic design, visual art, perceptual psychology and cognitive
science. The course is targeted both towards students interested in
using visualization in their own work, as well as students interested
in building better visualization tools and systems.
In addition to participating in class discussions,
students will have to complete several short programming and data
analysis assignments as well as a final programming project.

There are no official prerequisites for the class, but the class is
aimed at graduate students and advanced undergraduates. However,
familiarity with the material in CS147, CS 148 and CS142 can be
useful. Even more important is a basic working knowledge of, of some
web-programming and/or graphics API (e.g. Javascript/D3, Python,
WebGL). Experience with data analysis applications (e.g. Excel,
Matlab, R) is also helpful. The final project can be developed using
any suitable language or application. While we will cover a little bit
of Javascript/D3 in class, most of the other APIs, applications and
languages will not be taught in the course. However many introductory
tutorials at the level required for the class are available on the web
and we can help you find the relevant information as you need it.

Contact me (Maneesh) via
Piazza if you are
worried about whether you have the background for the course.

Assignments and Requirements

Late Policy: For assignments we will deduct 10% for each day (including weekends) the assignment is late.

Plagiarism Policy:
Assignments should consist primarily of your original work, building off of others’ work–including 3rd party libraries, public source code examples, and design ideas–is acceptable and in most cases encouraged. However, failure to cite such sources will result in score deductions proportional to the severity of the oversight.